Comparison of breast cancer survival in two populations: Ardabil, Iran and British Columbia, Canada

<p>Abstract</p> <p>Background</p> <p>Patterns in survival can provide information about the burden and severity of cancer, help uncover gaps in systemic policy and program delivery, and support the planning of enhanced cancer control systems. The aim of this paper is to describe the one-year survival rates for breast cancer in two populations using population-based cancer registries: Ardabil, Iran, and British Columbia (BC), Canada.</p> <p>Methods</p> <p>All newly diagnosed cases of female breast cancer were identified in the Ardabil cancer registry from 2003 to 2005 and the BC cancer registry for 2003. The International Classification of Disease for Oncology (ICDO) was used for coding cancer morphology and topography. Survival time was determined from cancer diagnosis to death. Age-specific one-year survival rates, relative survival rates and weighted standard errors were calculated using life-tables for each country.</p> <p>Results</p> <p>Breast cancer patients in BC had greater one-year survival rates than patients in Ardabil overall and for each age group under 60.</p> <p>Conclusion</p> <p>These findings support the need for breast cancer screening programs (including regular clinical breast examinations and mammography), public education and awareness regarding early detection of breast cancer, and education of health care providers.</p

Risk and Mortality of Recurrent Breast Cancer in Stockholm 1985-2005

The purpose of this study was to estimate the risk and mortality of breast cancer recurrences in Swedish women, and to analyse changes over time and variations between patients in different risk groups. Such estimates are of key importance for modelling the cost-effectiveness of different strategies for adjuvant treatment of breast cancer. The study was based on all women diagnosed with breast cancer in Stockholm County between 1985 and 2005. Information about dates for locoregional recurrences, metastatic relapses, new contralateral tumours and death was collected. Cox proportional hazard and Weibull regression models were used to estimate survival functions, where year of diagnosis (dived into 5-year intervals), were included as explanatory variables in the models. The risk of recurrences has decreased during the last 20 years for all three types of recurrence; for metastatic relapse the 5-year risk was reduced from 12.9% to 6.0% from 1985-90 to 2000-2005 . Mortality has also been reduced, resulting in an increased 5-year survival from 52.6% to 64.1% after locoregional recurrence and from 10.4% to 15.5% for metastatic relapse. For contralateral tumours, with a 5-year survival rate of 74.6% in 1985-1990 and 78% 2000-2005, no significant increase was observed. Analysis of risk groups according to TNM classification showed large difference in the risk of metastatic breast cancer between the three defined groups, but small differences for the risk of locoregional recurrences and new contralateral tumours. The findings indicate that the early detection and new treatments have been successful in improving outcome for breast cancer patients and that it is important to use up-to-date information, when assessing the value of new treatment options.Breast cancer; Mortality; Survival; Recurrence; Sweden

DECISION TREE CLASSIFIERS FOR CLASSIFICATION OF BREAST CANCER

Objective: Breast cancer is one of the dangerous cancers among world's women above 35 y. The breast is made up of lobules that secrete milk and thin milk ducts to carry milk from lobules to the nipple. Breast cancer mostly occurs either in lobules or in milk ducts. The most common type of breast cancer is ductal carcinoma where it starts from ducts and spreads across the lobules and surrounding tissues. Survey: According to the medical survey, each year there are about 125.0 per 100,000 new cases of breast cancer are diagnosed and 21.5 per 100,000 women due to this disease in united states. Also, 246,660 new cases of women with cancer are estimated for the year 2016.Methods: Early diagnosis of breast cancer is a key factor for long-term survival of cancer patients. Classification is one of the vital techniques used by researchers to analyze and classify the medical data.Results: This paper analyzes the different decision tree classifier algorithms for seer breast cancer dataset using WEKA software. The performance of the classifiers are evaluated against the parameters like accuracy, Kappa statistic, Entropy, RMSE, TP Rate, FP Rate, Precision, Recall, F-Measure, ROC, Specificity, Sensitivity.Conclusion: The simulation results shows REPTree classifier classifies the data with 93.63% accuracy and minimum RMSE of 0.1628 REPTree algorithm consumes less time to build the model with 0.929 ROC and 0.959 PRC values. By comparing classification results, we confirm that a REPTree algorithm is better than other classification algorithms for SEER dataset

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

© 2017 The Author(s). Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis

Huge decreases in the risk of breast cancer relapse over the last three decades

Introduction The aim of this study was to evaluate local and systemic breast cancer control by comparing the risk of relapse in breast cancer patients in 2003–2004 with that in 1972–1979 and in 1980–1986. Methods About 8,570 women diagnosed with invasive breast cancer in 2003–2004 were selected from the population-based Netherlands Cancer Registry and compared with 133 patients treated in 1972–1979 and 174 in 1980–1986. Five-year risk of relapse was calculated by the Kaplan–Meier method. Cox-proportional hazard models were applied to adjust for tumour size, nodal status and age at diagnosis. Results Patients diagnosed in 2003–2004 had smaller tumours and a less advanced nodal stage than patients diagnosed in 1972–1986. In 1972–1979, 1980–1986 and 2003–2004, treatment included mastectomy in 94%, 72% and 47%; postmastectomy radiotherapy in 75%, 70% and 30%; chemotherapy in 9%, 14% and 37% and hormonal therapy in 3%, 3% and 42% of patients, respectively. Five-year risk of locoregional and distant recurrence decreased from 37% and 34% to 15%, respectively. The 5-year risk of second primary breast cancer did not differ and was 1%, 4% and 2%, respectively. The improved relapse-free survival in patients diagnosed in 2003–2004 as compared with 1972–1979 hardly changed after adjustment (HR = 0.38, 95% CI = 0.28–0.52). Conclusion Over the last decades, local breast cancer therapies have become less rigorous, whereas systemic therapy use has increased. Simultaneously, the risk of breast cancer relapse has tremendously decreased. Future novel therapies may lead to such small additional decreases in relapse rates, while the long-term side effects in breast cancer survivors will increas

Sphingosine kinase 1 in breast cancer: A new molecular marker and a therapy target

It is now well-established that sphingosine kinase 1 (SK1) plays a significant role in breast cancer development, progression, and spread, whereas SK1 knockdown can reverse these processes. In breast cancer cells and tumors, SK1 was shown to interact with various pathways involved in cell survival and chemoresistance, such as nuclear factor-kappa B (NFκB), Notch, Ras/MAPK, PKC, and PI3K. SK1 is upregulated by estrogen signaling, which, in turn, confers cancer cells with resistance to tamoxifen. Sphingosine-1-phosphate (S1P) produced by SK1 has been linked to tumor invasion and metastasis. Both SK1 and S1P are closely linked to inflammation and adipokine signaling in breast cancer. In human tumors, high SK1 expression has been linked with poorer survival and prognosis. SK1 is upregulated in triple negative tumors and basal-like subtypes. It is often associated with high phosphorylation levels of ERK1/2, SFK, LYN, AKT, and NFκB. Higher tumor SK1 mRNA levels were correlated with poor response to chemotherapy. This review summarizes the up-to-date evidence and discusses the therapeutic potential for the SK1 inhibition in breast cancer, with emphasis on the mechanisms of chemoresistance and combination with other therapies such as gefitinib or docetaxel. We have outlined four key areas for future development, including tumor microenvironment, combination therapies, and nanomedicine. We conclude that SK1 may have a potential as a target for precision medicine, its high expression being a negative prognostic marker in ER-negative breast cancer, as well as a target for chemosensitization therapy

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival

An evaluation of DNA-damage response and cell-cycle pathways for breast cancer classification

Accurate subtyping or classification of breast cancer is important for ensuring proper treatment of patients and also for understanding the molecular mechanisms driving this disease. While there have been several gene signatures proposed in the literature to classify breast tumours, these signatures show very low overlaps, different classification performance, and not much relevance to the underlying biology of these tumours. Here we evaluate DNA-damage response (DDR) and cell cycle pathways, which are critical pathways implicated in a considerable proportion of breast tumours, for their usefulness and ability in breast tumour subtyping. We think that subtyping breast tumours based on these two pathways could lead to vital insights into molecular mechanisms driving these tumours. Here, we performed a systematic evaluation of DDR and cell-cycle pathways for subtyping of breast tumours into the five known intrinsic subtypes. Homologous Recombination (HR) pathway showed the best performance in subtyping breast tumours, indicating that HR genes are strongly involved in all breast tumours. Comparisons of pathway based signatures and two standard gene signatures supported the use of known pathways for breast tumour subtyping. Further, the evaluation of these standard gene signatures showed that breast tumour subtyping, prognosis and survival estimation are all closely related. Finally, we constructed an all-inclusive super-signature by combining (union of) all genes and performing a stringent feature selection, and found it to be reasonably accurate and robust in classification as well as prognostic value. Adopting DDR and cell cycle pathways for breast tumour subtyping achieved robust and accurate breast tumour subtyping, and constructing a super-signature which contains feature selected mix of genes from these molecular pathways as well as clinical aspects is valuable in clinical practice.Comment: 28 pages, 7 figures, 6 table

Disparities in Cause-Specific Cancer Survival by Census Tract Poverty Level in Idaho, U.S.

Objective. This population-based study compared cause-specific cancer survival by socioeconomic status using methods to more accurately assign cancer deaths to primary site. Methods. The current study analyzed Idaho data used in the Accuracy of Cancer Mortality Statistics Based on Death Certificates (ACM) study supplemented with additional information to measure cause-specific cancer survival by census tract poverty level. Results. The distribution of cases by primary site group differed significantly by poverty level (chi-square = 265.3, 100 df, p In the life table analyses, for 8 of 24 primary site groups investigated, and all sites combined, there was a significant gradient relating higher poverty with poorer survival. For all sites combined, the absolute difference in 5-year cause-specific survival rate was 13.6% between the lowest and highest poverty levels. Conclusions. This study shows striking disparities in cause-specific cancer survival related to the poverty level of the area a person resides in at the time of diagnosis